OpenRaiser/Intern-Atlas huggingface dataset:
From ATM to MPLS and QCI: The Evolution of Differentiated QoS Standards and Implications for 5G Network Slicing
Venue: arXiv cs.NI 2020
Year: 2020

The networking community continues to create new technologies and update existing ones to improve the quality, reliability, and "tailorability" of data networks. However, whenever Internet service providers attempt to productize "tailorability" and sell it explicitly to end customers as a premium service over best effort connectivity, they either fail to overcome net neutrality concerns or struggle to gain market traction. For this article, we focus only on those networking protocols, technologies, or standards whose goal is to offer tailored connectivity to paying customers on a public network and refer to them as differentiated QoS (D-QoS) standards. This article makes two contributions. First, it explores the techno-economic market trajectory of D-QoS standards to understand the factors that determine success. In doing this, we acknowledge that while there is wide variation and dissimilarity in their underlying technical properties, the expectation and goal for all D-QoS standards is that they will be used to provide guaranteed connection for which customers could be prepared to pay. As such, we consider Layer 2/3 technologies (e.g., ATM, frame relay, MPLS), signaling technologies (e.g., RSVP), data packet markers (e.g., IP ToS, DiffServ, WME, QCI), and end-to-end separation solutions (e.g., leased lines, network slicing) as a single cohort and analyze them together. Second, by exploring the parallels with 5G network slicing, we argue that despite its inherent technical differences with other D-QoS standards, the commercial performance of network slicing may end up resembling that of previous D-QoS standards. Consequently, we seek to learn lessons from previous D-QoS attempts and suggest that enterprise-focused 5G slices, running within a single service provider's domain and with binding service level agreements, will have the highest chance of success in the short/medium term.
Optimal tool path planning for 3D printing with spatio-temporal and thermal constraints
Venue: arXiv cs.RO 2020
Year: 2020

In this paper, we address the problem of synthesizing optimal path plans in a 2D subject to spatio-temporal and thermal constraints. Our solution consists of reducing the path planning problem to a Mixed Integer Linear Programming (MILP) problem. The challenge is in encoding the implication constraints in the path planning problem using only conjunctions that are permitted by the MILP formulation. Our experimental analysis using an implementation of the encoding in a Python toolbox demonstrates the feasibility of our approach in generating the optimal plans.
An unsupervised learning approach to solving heat equations on chip based on Auto Encoder and Image Gradient
Venue: arXiv cs.LG 2020
Year: 2020

Solving heat transfer equations on chip becomes very critical in the upcoming 5G and AI chip-package-systems. However, batches of simulations have to be performed for data driven supervised machine learning models. Data driven methods are data hungry, to address this, Physics Informed Neural Networks (PINN) have been proposed. However, vanilla PINN models solve one fixed heat equation at a time, so the models have to be retrained for heat equations with different source terms. Additionally, issues related to multi-objective optimization have to be resolved while using PINN to minimize the PDE residual, satisfy boundary conditions and fit the observed data etc. Therefore, this paper investigates an unsupervised learning approach for solving heat transfer equations on chip without using solution data and generalizing the trained network for predicting solutions for heat equations with unseen source terms. Specifically, a hybrid framework of Auto Encoder (AE) and Image Gradient (IG) based network is designed. The AE is used to encode different source terms of the heat equations. The IG based network implements a second order central difference algorithm for structured grids and minimizes the PDE residual. The effectiveness of the designed network is evaluated by solving heat equations for various use cases. It is proved that with limited number of source terms to train the AE network, the framework can not only solve the given heat transfer problems with a single training process, but also make reasonable predictions for unseen cases (heat equations with new source terms) without retraining.
Approaches to the implementation of generalized complex numbers in the Julia language
Venue: arXiv cs.MS 2020
Year: 2020

In problems of mathematical physics, to study the structures of spaces using the Cayley-Klein models in theoretical calculations, the use of generalized complex numbers is required. In the case of computational experiments, such tasks require their high-quality implementation in a programming language. The proposed small implementation of generalized complex numbers in modern programming languages have several disadvantages. In this article, we propose using the Julia language as the language for implementing generalized complex numbers, not least because it supports the multiple dispatch mechanism. The paper demonstrates the approach to the implementation of one of the types of generalized complex numbers, namely dual numbers. We place particular emphasis on the description of the use of the multiple dispatch mechanism to implement a new numerical type. The resulting implementation of dual numbers can be considered as a prototype for a complete software module for supporting generalized complex numbers.
ASAP-NMS: Accelerating Non-Maximum Suppression Using Spatially Aware Priors
Venue: arXiv cs.CV 2020
Year: 2020

The widely adopted sequential variant of Non Maximum Suppression (or Greedy-NMS) is a crucial module for object-detection pipelines. Unfortunately, for the region proposal stage of two/multi-stage detectors, NMS is turning out to be a latency bottleneck due to its sequential nature. In this article, we carefully profile Greedy-NMS iterations to find that a major chunk of computation is wasted in comparing proposals that are already far-away and have a small chance of suppressing each other. We address this issue by comparing only those proposals that are generated from nearby anchors. The translation-invariant property of the anchor lattice affords generation of a lookup table, which provides an efficient access to nearby proposals, during NMS. This leads to an Accelerated NMS algorithm which leverages Spatially Aware Priors, or ASAP-NMS, and improves the latency of the NMS step from 13.6ms to 1.2 ms on a CPU without sacrificing the accuracy of a state-of-the-art two-stage detector on COCO and VOC datasets. Importantly, ASAP-NMS is agnostic to image resolution and can be used as a simple drop-in module during inference. Using ASAP-NMS at run-time only, we obtain an mAP of 44.2\%@25Hz on the COCO dataset with a V100 GPU.
A 3D Tractable Model for UAV-Enabled Cellular Networks With Multiple Antennas
Venue: arXiv cs.IT 2020
Year: 2020

This paper aims to propose a three-dimensional (3D) point process model that can be employed to generally deploy unmanned aerial vehicles (UAVs) in a large-scale cellular network and tractably analyze the fundamental network-wide performances of the network. The proposed 3D point process is devised based on a 2D homogeneous marked Poisson point process (PPP) in which each point and its random mark uniquely correspond to the projection and the altitude of each point in the 3D point process, respectively. We study some of the important statistical properties of the proposed 3D point process and shed light on some crucial insights into these properties that facilitate the analyses of a UAV-enabled cellular network wherein all the UAVs equipped with multiple antennas are deployed by the proposed 3D point process to serve as aerial base stations. The salient features of the proposed 3D point process lie in its suitability in practical 3D channel modeling and tractability in analysis. The downlink coverage performances of the UAV-enabled cellular network are analyzed and found in neat expressions and their closed-form results for some special cases are also derived. Most importantly, their fundamental limits achieved by cell-free massive antenna array are characterized when coordinating all the UAVs to jointly perform non-coherent downlink transmission. Finally, numerical results are provided to validate some of the key findings in this paper.
Multimodal Dialogue State Tracking By QA Approach with Data Augmentation
Venue: arXiv cs.CL 2020
Year: 2020

Recently, a more challenging state tracking task, Audio-Video Scene-Aware Dialogue (AVSD), is catching an increasing amount of attention among researchers. Different from purely text-based dialogue state tracking, the dialogue in AVSD contains a sequence of question-answer pairs about a video and the final answer to the given question requires additional understanding of the video. This paper interprets the AVSD task from an open-domain Question Answering (QA) point of view and proposes a multimodal open-domain QA system to deal with the problem. The proposed QA system uses common encoder-decoder framework with multimodal fusion and attention. Teacher forcing is applied to train a natural language generator. We also propose a new data augmentation approach specifically under QA assumption. Our experiments show that our model and techniques bring significant improvements over the baseline model on the DSTC7-AVSD dataset and demonstrate the potentials of our data augmentation techniques.
A Manifold Learning Approach to Accelerate Phase Field Fracture Simulations in the Representative Volume Element
Venue: arXiv cs.CE 2020
Year: 2020

The multiscale simulation of heterogeneous materials is a popular and important subject in solid mechanics and materials science due to the wide application of composite materials. However, the classical FE2 (finite element2) scheme can be costly, especially when the microproblem is nonlinear. In this paper, we consider the case when the microproblem is the phase field formulation for fracture. We adopt the locally linear embedding (LLE) manifold learning approach, a method for non-linear dimension reduction, to extract the manifold that contains a collection of phase-field-represented initial microcrack patterns in the representative volume element (RVE). Then the output data corresponding to any other microcrack pattern, e.g., the evolved phase field at a fixed load, can be accurately reconstructed using the learned manifold with minimum computation. The method has two features: a minimum number of parameters for the scheme, and an input-specific error bar. The latter feature enables an adaptive strategy for any new input on whether to use the proposed, less expensive reconstruction, or to use an accurate but costly high-fidelity computation instead.
Energy-Efficient Non-Orthogonal Multicast and Unicast Transmission of Cell-Free Massive MIMO Systems with SWIPT
Venue: arXiv cs.IT 2020
Year: 2020

This work investigates the energy-efficient resource allocation for layered-division multiplexing (LDM) based non-orthogonal multicast and unicast transmission in cell-free massive multiple-input multiple-output (MIMO) systems, where each user equipment (UE) performs wireless information and power transfer simultaneously. To begin with, the achievable data rates for multicast and unicast services are derived in closed form, as well as the received radio frequency (RF) power at each UE. Based on the analytical results, a nonsmooth and nonconvex optimization problem for energy efficiency (EE) maximization is formulated, which is however a challenging fractional programming problem with complex constraints. To suit the massive access setting, a first-order algorithm is developed to find both initial feasible point and the nearly optimal solution. Moreover, an accelerated algorithm is designed to improve the convergence speed. Numerical results demonstrate that the proposed first-order algorithms can achieve almost the same EE as that of second-order approaches yet with much lower computational complexity, which provides insight into the superiority of the proposed algorithms for massive access in cell-free massive MIMO systems.
Gesture Recognition for Initiating Human-to-Robot Handovers
Venue: arXiv cs.CV 2020
Year: 2020

Human-to-Robot handovers are useful for many Human-Robot Interaction scenarios. It is important to recognize when a human intends to initiate handovers, so that the robot does not try to take objects from humans when a handover is not intended. We pose the handover gesture recognition as a binary classification problem in a single RGB image. Three separate neural network modules for detecting the object, human body key points and head orientation, are implemented to extract relevant features from the RGB images, and then the feature vectors are passed into a deep neural net to perform binary classification. Our results show that the handover gestures are correctly identified with an accuracy of over 90%. The abstraction of the features makes our approach modular and generalizable to different objects and human body types.
Quantifying Model Uncertainty in Inverse Problems via Bayesian Deep Gradient Descent
Venue: arXiv cs.CV 2020
Year: 2020

Recent advances in reconstruction methods for inverse problems leverage powerful data-driven models, e.g., deep neural networks. These techniques have demonstrated state-of-the-art performances for several imaging tasks, but they often do not provide uncertainty on the obtained reconstruction. In this work, we develop a scalable, data-driven, knowledge-aided computational framework to quantify the model uncertainty via Bayesian neural networks. The approach builds on, and extends deep gradient descent, a recently developed greedy iterative training scheme, and recasts it within a probabilistic framework. Scalability is achieved by being hybrid in the architecture: only the last layer of each block is Bayesian, while the others remain deterministic, and by being greedy in training. The framework is showcased on one representative medical imaging modality, viz. computed tomography with either sparse view or limited view data, and exhibits competitive performance with respect to state-of-the-art benchmarks, e.g., total variation, deep gradient descent and learned primal-dual.
Heterogeneous Verification of an Autonomous Curiosity Rover
Venue: arXiv cs.SE 2020
Year: 2020

The Curiosity rover is one of the most complex systems successfully deployed in a planetary exploration mission to date. It was sent by NASA to explore the surface of Mars and to identify potential signs of life. Even though it has limited autonomy on-board, most of its decisions are made by the ground control team. This hinders the speed at which the Curiosity reacts to its environment, due to the communication delays between Earth and Mars. Depending on the orbital position of both planets, it can take 4--24 minutes for a message to be transmitted between Earth and Mars. If the Curiosity were controlled autonomously, it would be able to perform its activities much faster and more flexibly. However, one of the major barriers to increased use of autonomy in such scenarios is the lack of assurances that the autonomous behaviour will work as expected. In this paper, we use a Robot Operating System (ROS) model of the Curiosity that is simulated in Gazebo and add an autonomous agent that is responsible for high-level decision-making. Then, we use a mixture of formal and non-formal techniques to verify the distinct system components (ROS nodes). This use of heterogeneous verification techniques is essential to provide guarantees about the nodes at different abstraction levels, and allows us to bring together relevant verification evidence to provide overall assurance.
Electre Tree A Machine Learning Approach to Infer Electre Tri B Parameters
Venue: arXiv cs.LG 2020
Year: 2020

Purpose: This paper presents an algorithm that can elicitate (infer) all or any combination of ELECTRE Tri-B parameters. For example, a decision-maker can maintain the values for indifference, preference, and veto thresholds, and our algorithm can find the criteria weights, reference profiles, and the lambda cutting level. Our approach is inspired by a Machine Learning ensemble technique, the Random Forest, and for that, we named our approach as ELECTRE Tree algorithm. Methodology: First, we generate a set of ELECTRE Tri-B models, where each model solves a random sample of criteria and alternatives. Each sample is made with replacement, having at least two criteria and between 10% to 25% of alternatives. Each model has its parameters optimized by a genetic algorithm that can use an ordered cluster or an assignment example as a reference to the optimization. Finally, after the optimization phase, two procedures can be performed, the first one will merge all models, finding in this way the elicitated parameters, and in the second procedure each alternative is classified (voted) by each separated model, and the majority vote decides the final class. Findings: We have noted that concerning the voting procedure, non-linear decision boundaries are generated, and they can be suitable in analyzing problems with the same nature. In contrast, the merged model generates linear decision boundaries. Originality: The elicitation of ELECTRE Tri-B parameters is made by an ensemble technique that is composed of a set of multicriteria models that are engaged in generating robust solutions.
Retracing some paths in categorical semantics: From process-propositions-as-types to categorified reals and computers
Venue: arXiv cs.LO 2020
Year: 2020

The logical parallelism of propositional connectives and type constructors extends beyond the static realm of predicates, to the dynamic realm of processes. Understanding the logical parallelism of process propositions and dynamic types was one of the central problems of the semantics of computation, albeit not always clear or explicit. It sprung into clarity through the early work of Samson Abramsky, where the central ideas of denotational semantics and process calculus were brought together and analyzed by categorical tools, e.g. in the structure of interaction categories. While some logical structures borne of dynamics of computation immediately started to emerge, others had to wait, be it because the underlying logical principles (mainly those arising from coinduction) were not yet sufficiently well-understood, or simply because the research community was more interested in other semantical tasks. Looking back, it seems that the process logic uncovered by those early semantical efforts might still be starting to emerge and that the vast field of results that have been obtained in the meantime might be a valley on a tip of an iceberg. In the present paper, I try to provide a logical overview of the gamut of interaction categories and to distinguish those that model computation from those that capture processes in general. The main coinductive constructions turn out to be of this latter kind, as illustrated towards the end of the paper by a compact category of all real numbers as processes, computable and uncomputable, with polarized bisimulations as morphisms. The addition of the reals arises as the biproduct, real vector spaces are the enriched bicompletions, and linear algebra arises from the enriched kan extensions. At the final step, I sketch a structure that characterizes the computable fragment of categorical semantics.
Wearable camera-based human absolute localization in large warehouses
Venue: arXiv cs.CV 2020
Year: 2020

In a robotised warehouse, as in any place where robots move autonomously, a major issue is the localization or detection of human operators during their intervention in the work area of the robots. This paper introduces a wearable human localization system for large warehouses, which utilize preinstalled infrastructure used for localization of automated guided vehicles (AGVs). A monocular down-looking camera is detecting ground nodes, identifying them and computing the absolute position of the human to allow safe cooperation and coexistence of humans and AGVs in the same workspace. A virtual safety area around the human operator is set up and any AGV in this area is immediately stopped. In order to avoid triggering an emergency stop because of the short distance between robots and human operators, the trajectories of the robots have to be modified so that they do not interfere with the human. The purpose of this paper is to demonstrate an absolute visual localization method working in the challenging environment of an automated warehouse with low intensity of light, massively changing environment and using solely monocular camera placed on the human body.
Learning Adaptive Sampling and Reconstruction for Volume Visualization
Venue: arXiv cs.GR 2020
Year: 2020

A central challenge in data visualization is to understand which data samples are required to generate an image of a data set in which the relevant information is encoded. In this work, we make a first step towards answering the question of whether an artificial neural network can predict where to sample the data with higher or lower density, by learning of correspondences between the data, the sampling patterns and the generated images. We introduce a novel neural rendering pipeline, which is trained end-to-end to generate a sparse adaptive sampling structure from a given low-resolution input image, and reconstructs a high-resolution image from the sparse set of samples. For the first time, to the best of our knowledge, we demonstrate that the selection of structures that are relevant for the final visual representation can be jointly learned together with the reconstruction of this representation from these structures. Therefore, we introduce differentiable sampling and reconstruction stages, which can leverage back-propagation based on supervised losses solely on the final image. We shed light on the adaptive sampling patterns generated by the network pipeline and analyze its use for volume visualization including isosurface and direct volume rendering.
Monte Carlo Dropout Ensembles for Robust Illumination Estimation
Venue: arXiv cs.CV 2020
Year: 2020

Computational color constancy is a preprocessing step used in many camera systems. The main aim is to discount the effect of the illumination on the colors in the scene and restore the original colors of the objects. Recently, several deep learning-based approaches have been proposed to solve this problem and they often led to state-of-the-art performance in terms of average errors. However, for extreme samples, these methods fail and lead to high errors. In this paper, we address this limitation by proposing to aggregate different deep learning methods according to their output uncertainty. We estimate the relative uncertainty of each approach using Monte Carlo dropout and the final illumination estimate is obtained as the sum of the different model estimates weighted by the log-inverse of their corresponding uncertainties. The proposed framework leads to state-of-the-art performance on INTEL-TAU dataset.